As in recent automatic augmentation literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [4]}, [8]}, [9]}, [10]}, [11]}, we focus on image classification datasets and employ a transformation search space comprising 14 operations, introduced by [12]}. For those that have parameters, their magnitudes are discretized in thirty levels, which does not compromise performance and greatly reduces the search space [5]}. Most approaches compose transformations, i.e. applying more than one transformation to the same image. However, recently [8]} have shown that applying only one transformation at a time, defined over a wider magnitude space (noted Wide in Table REF ) can outperform other approaches. We thus use the same transformation space as [8]}.
